from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE__		:       List[str]       						=       HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE__		:       Tuple       						=       parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE__		:       int       						=       AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE__		:       Optional[int]       						=       {
    "vocab_size": len(tokenizer),
    "scale_attn_by_inverse_layer_idx": True,
    "reorder_and_upcast_attn": True,
}

# Load model config (GPT-2 large in this case)
SCREAMING_SNAKE_CASE__		:       Any       						=       AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE__		:       Tuple       						=       AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case				:      int   		=			logging.get_logger(__name__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						str 							= DPTConfig(embedding_type="""hybrid""")

			if "large" in checkpoint_url:
						a_						:						List[str] 							= 1_0_2_4
						a_						:						List[str] 							= 4_0_9_6
						a_						:						Tuple 							= 2_4
						a_						:						str 							= 1_6
						a_						:						List[str] 							= [5, 1_1, 1_7, 2_3]
						a_						:						str 							= [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
						a_						:						Any 							= (1, 3_8_4, 3_8_4)

			if "nyu" or "midas" in checkpoint_url:
						a_						:						int 							= 7_6_8
						a_						:						List[str] 							= [1, 1, 1, 0.5]
						a_						:						Tuple 							= [2_5_6, 5_1_2, 7_6_8, 7_6_8]
						a_						:						Tuple 							= 1_5_0
						a_						:						Optional[Any] 							= 1_6
						a_						:						Union[str, Any] 							= (1, 3_8_4, 3_8_4)
						a_						:						Optional[int] 							= False
						a_						:						int 							= """project"""

			if "ade" in checkpoint_url:
						a_						:						Union[str, Any] 							= True
						a_						:						int 							= 7_6_8
						a_						:						Dict 							= [1, 1, 1, 0.5]
						a_						:						Optional[int] 							= 1_5_0
						a_						:						Tuple 							= 1_6
						a_						:						str 							= """huggingface/label-files"""
						a_						:						List[Any] 							= """ade20k-id2label.json"""
						a_						:						Tuple 							= json.load(open(cached_download(hf_hub_url(a__  ,	a__  ,	repo_type="""dataset"""))  ,	"""r"""))
						a_						:						Optional[Any] 							= {int(a__): v for k, v in idalabel.items()}
						a_						:						str 							= idalabel
						a_						:						Optional[Any] 							= {v: k for k, v in idalabel.items()}
						a_						:						Optional[int] 							= [1, 1_5_0, 4_8_0, 4_8_0]

			return config, expected_shape


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Any 							= ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
			for k in ignore_keys:
						state_dict.pop(a__  ,	a__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			if (
			    "pretrained.model" in name
			    and "cls_token" not in name
			    and "pos_embed" not in name
			    and "patch_embed" not in name
			):
						a_						:						Dict 							= name.replace("""pretrained.model"""  ,	"""dpt.encoder""")
			if "pretrained.model" in name:
						a_						:						List[Any] 							= name.replace("""pretrained.model"""  ,	"""dpt.embeddings""")
			if "patch_embed" in name:
						a_						:						List[str] 							= name.replace("""patch_embed"""  ,	"""""")
			if "pos_embed" in name:
						a_						:						Tuple 							= name.replace("""pos_embed"""  ,	"""position_embeddings""")
			if "attn.proj" in name:
						a_						:						Optional[int] 							= name.replace("""attn.proj"""  ,	"""attention.output.dense""")
			if "proj" in name and "project" not in name:
						a_						:						Tuple 							= name.replace("""proj"""  ,	"""projection""")
			if "blocks" in name:
						a_						:						Any 							= name.replace("""blocks"""  ,	"""layer""")
			if "mlp.fc1" in name:
						a_						:						Tuple 							= name.replace("""mlp.fc1"""  ,	"""intermediate.dense""")
			if "mlp.fc2" in name:
						a_						:						List[str] 							= name.replace("""mlp.fc2"""  ,	"""output.dense""")
			if "norm1" in name and "backbone" not in name:
						a_						:						Dict 							= name.replace("""norm1"""  ,	"""layernorm_before""")
			if "norm2" in name and "backbone" not in name:
						a_						:						str 							= name.replace("""norm2"""  ,	"""layernorm_after""")
			if "scratch.output_conv" in name:
						a_						:						List[Any] 							= name.replace("""scratch.output_conv"""  ,	"""head""")
			if "scratch" in name:
						a_						:						Optional[int] 							= name.replace("""scratch"""  ,	"""neck""")
			if "layer1_rn" in name:
						a_						:						List[Any] 							= name.replace("""layer1_rn"""  ,	"""convs.0""")
			if "layer2_rn" in name:
						a_						:						Any 							= name.replace("""layer2_rn"""  ,	"""convs.1""")
			if "layer3_rn" in name:
						a_						:						List[Any] 							= name.replace("""layer3_rn"""  ,	"""convs.2""")
			if "layer4_rn" in name:
						a_						:						Optional[Any] 							= name.replace("""layer4_rn"""  ,	"""convs.3""")
			if "refinenet" in name:
						a_						:						Tuple 							= int(name[len("""neck.refinenet""") : len("""neck.refinenet""") + 1])
						# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
						a_						:						List[Any] 							= name.replace(f'''refinenet{layer_idx}'''  ,	f'''fusion_stage.layers.{abs(layer_idx-4)}''')
			if "out_conv" in name:
						a_						:						List[Any] 							= name.replace("""out_conv"""  ,	"""projection""")
			if "resConfUnit1" in name:
						a_						:						List[str] 							= name.replace("""resConfUnit1"""  ,	"""residual_layer1""")
			if "resConfUnit2" in name:
						a_						:						Optional[int] 							= name.replace("""resConfUnit2"""  ,	"""residual_layer2""")
			if "conv1" in name:
						a_						:						List[str] 							= name.replace("""conv1"""  ,	"""convolution1""")
			if "conv2" in name:
						a_						:						Optional[Any] 							= name.replace("""conv2"""  ,	"""convolution2""")
			# readout blocks
			if "pretrained.act_postprocess1.0.project.0" in name:
						a_						:						List[Any] 							= name.replace("""pretrained.act_postprocess1.0.project.0"""  ,	"""neck.reassemble_stage.readout_projects.0.0""")
			if "pretrained.act_postprocess2.0.project.0" in name:
						a_						:						str 							= name.replace("""pretrained.act_postprocess2.0.project.0"""  ,	"""neck.reassemble_stage.readout_projects.1.0""")
			if "pretrained.act_postprocess3.0.project.0" in name:
						a_						:						List[str] 							= name.replace("""pretrained.act_postprocess3.0.project.0"""  ,	"""neck.reassemble_stage.readout_projects.2.0""")
			if "pretrained.act_postprocess4.0.project.0" in name:
						a_						:						Any 							= name.replace("""pretrained.act_postprocess4.0.project.0"""  ,	"""neck.reassemble_stage.readout_projects.3.0""")

			# resize blocks
			if "pretrained.act_postprocess1.3" in name:
						a_						:						Dict 							= name.replace("""pretrained.act_postprocess1.3"""  ,	"""neck.reassemble_stage.layers.0.projection""")
			if "pretrained.act_postprocess1.4" in name:
						a_						:						Dict 							= name.replace("""pretrained.act_postprocess1.4"""  ,	"""neck.reassemble_stage.layers.0.resize""")
			if "pretrained.act_postprocess2.3" in name:
						a_						:						int 							= name.replace("""pretrained.act_postprocess2.3"""  ,	"""neck.reassemble_stage.layers.1.projection""")
			if "pretrained.act_postprocess2.4" in name:
						a_						:						List[Any] 							= name.replace("""pretrained.act_postprocess2.4"""  ,	"""neck.reassemble_stage.layers.1.resize""")
			if "pretrained.act_postprocess3.3" in name:
						a_						:						Union[str, Any] 							= name.replace("""pretrained.act_postprocess3.3"""  ,	"""neck.reassemble_stage.layers.2.projection""")
			if "pretrained.act_postprocess4.3" in name:
						a_						:						Dict 							= name.replace("""pretrained.act_postprocess4.3"""  ,	"""neck.reassemble_stage.layers.3.projection""")
			if "pretrained.act_postprocess4.4" in name:
						a_						:						Tuple 							= name.replace("""pretrained.act_postprocess4.4"""  ,	"""neck.reassemble_stage.layers.3.resize""")
			if "pretrained" in name:
						a_						:						List[Any] 							= name.replace("""pretrained"""  ,	"""dpt""")
			if "bn" in name:
						a_						:						Optional[int] 							= name.replace("""bn"""  ,	"""batch_norm""")
			if "head" in name:
						a_						:						Union[str, Any] 							= name.replace("""head"""  ,	"""head.head""")
			if "encoder.norm" in name:
						a_						:						Any 							= name.replace("""encoder.norm"""  ,	"""layernorm""")
			if "auxlayer" in name:
						a_						:						Any 							= name.replace("""auxlayer"""  ,	"""auxiliary_head.head""")
			if "backbone" in name:
						a_						:						Optional[Any] 							= name.replace("""backbone"""  ,	"""backbone.bit.encoder""")

			if ".." in name:
						a_						:						str 							= name.replace(""".."""  ,	""".""")

			if "stem.conv" in name:
						a_						:						Union[str, Any] 							= name.replace("""stem.conv"""  ,	"""bit.embedder.convolution""")
			if "blocks" in name:
						a_						:						Union[str, Any] 							= name.replace("""blocks"""  ,	"""layers""")
			if "convolution" in name and "backbone" in name:
						a_						:						Dict 							= name.replace("""convolution"""  ,	"""conv""")
			if "layer" in name and "backbone" in name:
						a_						:						Optional[int] 							= name.replace("""layer"""  ,	"""layers""")
			if "backbone.bit.encoder.bit" in name:
						a_						:						Union[str, Any] 							= name.replace("""backbone.bit.encoder.bit"""  ,	"""backbone.bit""")
			if "embedder.conv" in name:
						a_						:						List[str] 							= name.replace("""embedder.conv"""  ,	"""embedder.convolution""")
			if "backbone.bit.encoder.stem.norm" in name:
						a_						:						str 							= name.replace("""backbone.bit.encoder.stem.norm"""  ,	"""backbone.bit.embedder.norm""")
			return name


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			for i in range(config.num_hidden_layers):
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						a_						:						Optional[int] 							= state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight''')
						a_						:						str 							= state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias''')
						# next, add query, keys and values (in that order) to the state dict
						a_						:						Any 							= in_proj_weight[: config.hidden_size, :]
						a_						:						List[str] 							= in_proj_bias[: config.hidden_size]
						a_						:						List[str] 							= in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						a_						:						str 							= in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						a_						:						Any 							= in_proj_weight[
						    -config.hidden_size :, :
						]
						a_						:						List[str] 							= in_proj_bias[-config.hidden_size :]


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						Dict 							= """http://images.cocodataset.org/val2017/000000039769.jpg"""
			a_						:						Union[str, Any] 							= Image.open(requests.get(a__  ,	stream=a__).raw)
			return im


@torch.no_grad()
def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_							,					a_						:						str 							= get_dpt_config(a__)
			# load original state_dict from URL
			# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
			a_						:						str 							= torch.load(a__  ,	map_location="""cpu""")
			# remove certain keys
			remove_ignore_keys_(a__)
			# rename keys
			for key in state_dict.copy().keys():
						a_						:						List[str] 							= state_dict.pop(a__)
						a_						:						str 							= val
			# read in qkv matrices
			read_in_q_k_v(a__  ,	a__)

			# load HuggingFace model
			a_						:						Any 							= DPTForSemanticSegmentation(a__) if """ade""" in checkpoint_url else DPTForDepthEstimation(a__)
			model.load_state_dict(a__)
			model.eval()

			# Check outputs on an image
			a_						:						Any 							= 4_8_0 if """ade""" in checkpoint_url else 3_8_4
			a_						:						Any 							= DPTImageProcessor(size=a__)

			a_						:						Dict 							= prepare_img()
			a_						:						int 							= image_processor(a__  ,	return_tensors="""pt""")

			# forward pass
			a_						:						Optional[Any] 							= model(**a__).logits if """ade""" in checkpoint_url else model(**a__).predicted_depth

			if show_prediction:
						a_						:						List[str] 							= (
						    torch.nn.functional.interpolate(
						        outputs.unsqueeze(1)  ,	size=(image.size[1], image.size[0])  ,	mode="""bicubic"""  ,	align_corners=a__  ,	)
						    .squeeze()
						    .cpu()
						    .numpy()
						)

						Image.fromarray((prediction / prediction.max()) * 2_5_5).show()

			if pytorch_dump_folder_path is not None:
						Path(a__).mkdir(exist_ok=a__)
						print(f'''Saving model to {pytorch_dump_folder_path}''')
						model.save_pretrained(a__)
						print(f'''Saving image processor to {pytorch_dump_folder_path}''')
						image_processor.save_pretrained(a__)

			if push_to_hub:
						model.push_to_hub("""ybelkada/dpt-hybrid-midas""")
						image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas""")


if __name__ == "__main__":
	__snake_case				:      str   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--checkpoint_url""",
	    default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
	    type=str,
	    help="""URL of the original DPT checkpoint you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""",
	    default=None,
	    type=str,
	    required=False,
	    help="""Path to the output PyTorch model directory.""",
	)
	parser.add_argument(
	    """--push_to_hub""",
	    action="""store_true""",
	)
	parser.add_argument(
	    """--model_name""",
	    default="""dpt-large""",
	    type=str,
	    help="""Name of the model, in case you're pushing to the hub.""",
	)
	parser.add_argument(
	    """--show_prediction""",
	    action="""store_true""",
	)

	__snake_case				:      str   		=			parser.parse_args()
	convert_dpt_checkpoint(
	    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
	)


